Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review
It provides a practical guide for radiologists to mitigate risks in AI adoption, but is incremental as it reviews existing knowledge rather than introducing novel methods.
This paper addresses the need for radiologists to understand AI model generalization issues in medical imaging, highlighting pitfalls and considerations for safe deployment without presenting new experimental results.
The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data they are trained on, and the settings in which they will be deployed. As with all new technologies, use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help. This case-based review is intended to point out specific factors practicing radiologists who intend to use AI should consider.